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Urban Object Extraction from Digital Surface Model and Digital Aerial Images : Volume I-3, Issue 1 (20/07/2012)

By Grigillo, D.

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Book Id: WPLBN0004013593
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Urban Object Extraction from Digital Surface Model and Digital Aerial Images : Volume I-3, Issue 1 (20/07/2012)  
Author: Grigillo, D.
Volume: Vol. I-3, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Kanjir, U., & Grigillo, D. (2012). Urban Object Extraction from Digital Surface Model and Digital Aerial Images : Volume I-3, Issue 1 (20/07/2012). Retrieved from

Description: Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova cesta 2, Ljubljana, Slovenia. The paper describes two different methods for extraction of two types of urban objects from lidar digital surface model (DSM) and digital aerial images. Within the preprocessing digital terrain model (DTM) and orthoimages for three test areas were generated from aerial images using automatic photogrammetric methods. Automatic building extraction was done using DSM and multispectral orthoimages. First, initial building mask was created from the normalized digital surface model (nDSM), then vegetation was eliminated from the building mask using multispectral orthoimages. The final building mask was produced employing several morphological operations and buildings were vectorised using Hough transform. Automatic extraction of other green urban features (trees and natural ground) started from orthoimages using iterative object-based classification. This method required careful selection of segmentation parameters; in addition to basic spectral bands also information from nDSM was included. After the segmentation of images the segments were classified based on their attributes (spatial, spectral, geometrical, texture) using rule set classificator. First iteration focused on visible (i.e. unshaded) urban features, and second iteration on objects in deep shade. Results from both iterations were merged into appropriate classes. Evaluation of the final results (completeness, correctness and quality) was carried out on a per-area level and on a per-object level by ISPRS Commission III, WG III/4.



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